Ship Operation Research Articles

Relevance of international legal and national standards of safety of navigation and the role of classification societies in the context of modern challenges and innovations in shipbuilding

The relevance of international legal and national safety standards for shipping is growing due to modern challenges, such as innovations in shipbuilding, climate change and growing requirements for environmental safety, which underlines the important role of classification societies in this process. The purpose of the study was to analyse the impact of international and national safety standards on modern shipbuilding and to assess the role of classification societies in ensuring the safe operation of ships. The study applied the methods of system analysis, comparative law and empirical modelling. The main results of the study are the identification of the need for further harmonization of international legal standards with national requirements, improvement of the role of classification societies in monitoring innovations in shipbuilding, and formulation of proposals for improving the environmental safety of shipping. In addition, the paper identifies the prospects for using the latest technologies to improve safety standards in maritime transport. In particular, the article focuses on the role of classification societies in ensuring that ships comply with these standards, as well as their contribution to the introduction of innovations in shipbuilding. The study contains an analysis of current regulations and standards governing shipping safety, as well as cases of their implementation in practice. The key changes in ship safety requirements in view of current risks, including environmental challenges and increased quality requirements, are identified. The practical value of the work lies in the possibility of using the results obtained by both regulatory authorities and shipbuilding companies to improve internal safety standards and support international certification

A wall climbing robot based on machine vision for automatic welding seam inspection

With the ongoing progress of industrial technology such as shipbuilding, the importance of weld quality in industrial production is becoming increasingly prominent. Intelligent and automated welding seam inspection robots are more efficient than traditional manual inspection and can avoid dangerous accidents. This article describes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robot uses machine vision and object segmentation models to automatically detect the position of welding seams, and uses a cubic polynomial to fit the welding seam path. The upper and lower computers of the robot communicate through WIFI transmission and TCP protocol, which can realize remote real-time detection of weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robot high-altitude wall climbing, which has been verified through simulation and experimental verification. To verify the intelligence of the robot, this paper conducted performance analysis experiments on weld line recognition and tracking models and surface defect models. The experimental results showed that the average detection accuracy of the weld line recognition and tracking algorithm was 96.8%, and the average detection accuracy of surface defects in the three types of welds was 94.2%. The method proposed in this article combines two algorithms and a special robot structure, providing a new approach for the automated inspection of welds in large industrial products such as ships.

Study on comprehensive evaluation method of mental workload level

Abstract Mental workload has an important impact on the human performance of ship operators. The purpose of this study is to develop an evaluation method to evaluate the ship operator’s mental workload. First, an evaluation index system was constructed, and the DEMATEL (decision-making trial and evaluation laboratory) method was used to determine the relative weight of each index. Then, the fuzzy evaluation method was used to calculate the mental workload of ship operator based on the evaluation value and relative weight of indexes. The evaluation result of the developed method showed that the ship operators’ mental workload were 3.78 and 2.99 under tasks 1 and 2, and the ship operators’ mental workloads were at the ‘very high’ and ‘high’ levels based on the maximum membership principle. Also, the evaluation result of the developed method was consistent with the evaluation results of subjective mental workload assessment technique. It proves that the developed method can effectively evaluate the ship operator’s mental workload level.

Short-Term Photovoltaic Power Prediction Based on a Digital Twin Model

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Enhanced predictive modeling vs. LCA simulation: A comparative study on CO2 emissions from ship operations

The shipping industry's reliance on fossil fuels significantly contributes to increased greenhouse gas emissions, which is a global issue. Thus, to reduce these emissions, the International Maritime Organization has developed numerous regulations and technical measures. However, these rely on simple estimates derived from ship types and fuel consumption, without considering detailed external conditions. This case study develops a predictive model for carbon dioxide emissions from the exhaust gases of a ship during actual operation using statistical methods based on the telegraph modes of its speed. The results are compared and analyzed with Life Cycle Assessment (LCA) simulation outcomes. The developed predictive model and LCA simulation results confirmed that carbon dioxide emission amounts increase as revolutions per minute rise across various telegraph modes. This result indicates a corresponding increase in fuel consumption. The model predicted levels approximately 83% higher than those obtained from LCA simulations. These differences occurred due to a failure to consider factors such as engine characteristics and external forces acting on the ship in LCA simulations. Furthermore, this study applies ANCOVA analysis to examine the predictive model and LCA simulation trends, confirming that the groups' trends were statistically equal. Despite a quantitative difference between the actual outcomes based on the predictive model and LCA simulation, the latter still provides a suitable and quick way to analyze different case types to make sustainable choices.

Basic Study for Grasping the Human Gaze Movement of a Remoting-Ship Operator

Our research focuses on understanding the disparity in risk perception when operating maritime autonomous surface ships (MASS) between onboard and through screens. In our initial step, we conducted experiments using a ship simulator, involving 4 students aspiring to be navigators. They navigated through screens and participated in debriefing sessions. Human gaze movement was utilized to ascertain the ability to comprehend navigational risks solely through observation. Simultaneously, salivary alpha-amylase samples were taken to verify their accurate risk cognition. The key finding indicates that human gaze movement aids in precisely understanding navigational risks, suggesting the feasibility of future ship operations through screens. Our research targets navigators crucial in global logistics, aiming to enhance safety in remote ship operations by demonstrating that risk cognition is achievable through human gaze movement. This breakthrough is vital for the evolving realm of MASS operations.

Data collection framework for enhanced carbon intensity indicator (CII) in the oil tankers

AbstractThe International Maritime Organization (IMO) aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 compared with 2008. The carbon intensity indicator (CII) calculates the annual reduction factor required to continuously improve a ship's operational carbon intensity at a specific rating level. Verification and documentation of the achieved annual operational CII against the prescribed target are necessary to establish the operational carbon intensity rating. This study focuses on the intricate process of data collection for CII within the oil shipping industry, targeting engineering departments and shipboard management teams. Against the backdrop of the industry's substantial carbon dioxide emissions, the IMO has mandated the calculation of CII values for ships exceeding 5000 gross tons to promote sustainability and reduce environmental impact. We have collected emission data of 20 oil tankers over a period of 2 years using our ship maintenance and operating system (SMOS) and analyzed the data to compare the CII ratings. Our results indicate that a staggering ~63% of the vessels had the lowest CII rating of category E. It is therefore crucial to properly collect, organize, and evaluate data for CII calculation and take necessary measures to improve rating. This paper provides a deeper insight into the evolving CII calculation methodology, emphasizing the incorporation of correction factors and exclusions, and delineates the essential data collection practices needed to facilitate accurate CII calculations.

Design of Ship Trajectory Control Method Integrating Self Disturbance Rejection and Neural Network

To reduce the ship trajectory control difficulty, a ship trajectory control method based on active disturbance rejection control and radial basis function neural network is designed. Firstly, a separated model structure is proposed to model the ship’s navigation system, followed by the introduction of active disturbance rejection control technology to control the ship’s trajectory, and the fusion of radial basis function to improve the parameter adjustment effect. It was found that on the AIS and MSSIS datasets when the system was iterated 25 and 22 times, respectively, the fitness values of the research method were as high as 99.46 and 99.51. In addition, when the accuracy of all algorithms was 0.900, the recall rate of the research method was significantly the highest, at 0.752. When the recall rate was 0.900, the accuracy of the research method was significantly the highest, reaching 0.869. In practical applications, when there was no external interference, the heading was a square wave signal, and when the time reached 90.11, the proposed method operated with a small difference between the planned heading and the actual heading. In addition, it was found that the control effect of the research method on ship operation remains highly stable when external interference is added during ship operation. The above results indicate that the designed method has significant advantages in ship trajectory control tasks and can effectively enhance the navigation safety and stability of ships.

Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities

The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.

A Scoping Review on Occupational Noise Mitigation Strategies and Recommendations for Sustainable Ship Operations.

Environmental and occupational noise has the potential to result in health risks. The presence of high noise levels aboard ships can cause substantial hazards that affect the well-being of those employed in the maritime industry. The study and implementation of occupational noise reduction aboard ships are of the highest priority for ensuring the well-being of marine workers, compliance with regulatory standards, protection of the environment, and improvement of overall operational efficiency and safety within the maritime sector. A scoping study was conducted to collect and summarize the existing scientific literature about approaches to preventing occupational noise in vessel operations. We searched electronic databases for papers published up to June 2024. Initially, 94 articles were identified for screening, and the present research produced 16 studies, which were finally analyzed. Resultantly, noise control may begin with elimination, substitution, engineering, administrative, and hearing protection (ear plugs or muffs). Noise control innovation would be started with engineering techniques. Hearing protection devices (HPDs) could be used to reduce noise and as an instrument of communication between sailors. More research needs to be carried out in order to find the best ways for maritime vessels to reduce noise at work and to see how well they work in lowering the risks that come with noise for workers on board.

Rancang Bangun Prototipe Pengisian Air Expansion Tank Berbasis IoT

The shipping industry faces challenges in reducing environmental impact and increasing operational efficiency. Among them are the expansion tanks on ship engines which play an important role in maintaining the pressure and water quality of the engine cooling system, which is still run manually on some ships. This can result in the risk of engine damage due to overheating or unstable pressure. Manual water filling that requires crew supervision can cause operational instability. This research designed a prototype of an automatic water filling system for expansion tanks, using microcontroller-based automation technology and an Internet of things system to make monitoring easier. This system aims to increase efficiency and reduce the risk of engine damage, thereby supporting the sustainability of ship operations in the future. In the trials that have been carried out, this tool is able to operate the expansion tank filling automatically and efficiently. The distance sensor used has high accuracy with a largest error of 1.6%, and an average error of 0.01%. Integration with the Internet of Things system can run well and reliably with 100% success and latency of 0.5 – 1 second. The system's overall success rate is 80%. So the system can be used in real working conditions

The impact of opening the Arctic Northeast Passage on China's carbon emissions

The increase in shipping along Arctic routes has resulted in an increasing need for the environmental effects of shipping to be considered. Existing studies on the environmental impact of Arctic routes have focused mainly on shipping operations and have paid little attention to the dynamic features of opening new shipping routes. In light of the continuing retreat of Arctic sea ice, this study aimed to predict changes in China's CO2 emissions from a production perspective via scenarios of the opening of the Arctic Northeast Passage (NEP) in different future years (2025, 2030, 2035). A shipping cost model was first developed to calculate the changes in shipping costs resulting from commercial use of the NEP. Then, an energy-environmental version of the Global Trade Analysis Project (GTAP-E) model and the GTAP-E 10.0 database were used to simulate the changes in CO2 emissions that may arise from the economic adjustments to the opening of the NEP. The driving factors and possible mechanisms of the environmental effects of the NEP were analyzed based on the simulation results. The re-simulation for the environmental effects of opening the NEP taking emission control policies into account was performed. The findings showed that the opening of the NEP would increase global CO2 emissions. This effect would gradually increase over time. For China, opening the NEP would initially reduce short- and medium-term CO2 emissions owing to upgrades of sectoral structure, while ultimately elevating emissions in the long run due to a large expansion in trade and the economy. The economic growth driven by commercial use of the NEP would tend to optimize the Chinese sectoral and energy structure in the short run. Notably, emission control policies would markedly affect the aforementioned environmental outcomes of opening the NEP.

Release behavior of antifouling materials based on intercalated layered double hydroxides

Improving the utilization rate of environmentally friendly antifouling agents is highly important for reducing the economic costs of ship operations and protecting the marine environment. In this work, a new method for preparing a multistage controlled release material of an environmentally friendly antifouling agent, which first incorporates 2-(p-chlorophenyl)-3-cyano-4-bromo-5-trifluoromethyl pyrrole (Econea) into carboxyl-modified β-cyclodextrin (Econea@CMCD) followed by intercalation into Mg2Al layered double hydroxides (Mg2Al-Econea@CMCD-LDH), was reported. Powder X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) confirmed the incorporation of Econea@CMCD into the galleries of Mg2Al-LDH. Release tests conducted in 3.5 % NaCl solution demonstrated an initial burst release, followed by a slower release rate of Econea (12.3 µg/cm2·d) compared to that of the pristine Econea coating, indicating favorable controlled release behavior. Adhesion experiments using diatoms and mussels showed that Mg2Al-Econea@CMCD-LDH effectively inhibited the adhesion of typical fouling organisms. Hence, this study presents a practical approach for constructing a controlled release system that involves intercalating neutral and insoluble antifouling agents into LDH interlayers, effectively reducing the waste of antifouling agents and extending the service life of antifouling coatings.

An exploratory study of approaches to onboard training in different countries

ABSTRACT This study explores how current shipboard/onboard training arrangements vary among countries and what informs these differences. The study – employing qualitative and quantitative research approaches in the form of an online survey and in-depth semi-structured interviews – collected data on onboard training arrangements from 95 MET institutions from 44 countries. From the data analysis, onboard training arrangements were categorised into seven dominant types based on regional and institutional differences with respect to four elements: substitution by simulator training, integration into the academic programme, schedule, and type of ships used. The analysis further explored the different rationales for the diverse approaches to onboard training and the dominance of the approaches in different regions. Although not explicitly mentioned by research participants, a variety of learning theories can explain the reasons underpinning the choice of particular onboard training strategies and these are discussed. Given the fundamental differences in onboard training strategies identified in this study, future research should first examine the issues and difficulties that confront the implementation of onboard training and the factors/trends that actually and/or potentially influence such implementation. Secondly, future research should explore the development of a model that can comprehensively address the challenge of onboard training across countries and do so with current trends in mind, in particular, the increasing fidelity and abilities of simulation for seafarer education and training as well as developments in ship operation with increasing levels of automation.

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

Analyzing ship collision accidents in China: A framework based on the N-K model and Bayesian networks

Maritime accidents are frequent in China, with collisions constituting a significant portion, leading to substantial casualties, financial losses, and environmental harm. Analyzing collision risk is crucial for safe shipping operations, as such accidents arise when multiple risk factors interact beyond certain thresholds. Thus, understanding the coupled relationships among these factors is essential for accident prevention. This study presents a quantitative coupling risk assessment approach for collisions using the N–K model and Bayesian networks (BNs). Through an analysis of 304 accident reports from the China Maritime Safety Administration (CMSA), four primary risk factors (human, ship, environment, and management) are identified. The N–K model quantifies the coupling degree between these factors, while a BN model further elucidates and quantifies the collision risk based on the calculations of N–K model. Validation of the BN model is followed by sensitivity analysis to assess the impact of risk factors on accident occurrence. Subsequently, the method is applied to a case study on collision risk assessment in Chinese waters. Findings indicate that multifactor coupling has a higher occurrence probability than two-factor coupling, with human-ship-environment-management coupling presenting the highest likelihood. Human and management factors emerge as pivotal in collision incidents, while the impact of ship and environmental factors on coupling becomes more pronounced as their probabilities vary. Leveraging the capability of BNs to handle uncertainties and logical relationships, combined with the data-driven N–K model, mitigates subjectivity. The results propose the formulation of risk management strategies and measures for practitioners to enhance maritime safety.

Bulbous Bow Shapes Effect on Ship Characteristics: A Review

Abstract Hydrodynamic optimization is an effective and robust design method that is indispensable in ship hull form performance. Marine engineering and naval architecture have displayed significant interest in integrating bulbous bows on combatant ships. This review aims to gather and assess the existing knowledge regarding the effects of bulbous bow shapes on the hydrodynamic characteristics of combatant ships in terms of historical background, design principles, and impacts of bulbous bow shapes on ship resistance, ship motion in seaways, squat, and seakeeping performance. The review evaluates the effects of bulbous bow shapes on hull hydrodynamics using computational fluid dynamics (CFD), model testing, and full-scale experiments. The text delves into the complexities of improving hull shape and how the bulbous bow design interacts with many operational factors like draft, speed, and sea conditions. This research offers scholars, naval architects, and marine engineers a comprehensive insight into the intricate effects of bulbous bow shapes on combatant ship performance. It aims to consolidate current material to enhance our comprehension of ship design and operation by identifying knowledge gaps and suggesting future research areas.

Efforts to prevent oil spills in bunker supply activities at Pertamina Trans Kontinental Plaju Branch

This study aims to identify and understand the consequences of marine pollution caused by oil spills from the operation of ships used for maritime transport. Data collection methods include library research, documentation, and descriptive data from various sources. The study highlights the importance of effective pollution prevention strategies to minimize the negative impacts of oil spills on marine ecosystems and aquatic environments. The main issue addressed is the prevention of oil spills before they spread and cause extensive damage to ecosystems. The theoretical framework used is human resource management theory related to safety and marine pollution prevention. Data collection methods include observation, interviews, and literature review. Data analysis is conducted through triangulation and continuously until data saturation is achieved. The results indicate that regular training on oil spill handling for all workers is essential. Additionally, the mandatory use of personal protective equipment, such as safety shoes, helmets, and special life jackets, is crucial to prevent fatal consequences from work accidents.

IMPACT OF MAJOR CRISES ON CONTAINER SHIPPING IN TÜRKİYE: THE CASE OF THE COVID-19 OUTBREAK

The Covid-19 outbreak is a recent major crisis with a broad and lasting impact on the economy, health, and society, rather than an immediate and temporary impact. In the first months following the declaration of the pandemic, the container shipping sector in Türkiye was impacted by restrictions and quarantine measures. During this period, there were reductions in ship calls, slowdowns in ship operations, and losses in storage and warehousing services. Although container volumes in Türkiye were less impacted than the global average, the previously stable growth trend came to a halt. This study examines the impact of the Covid-19 outbreak on container shipping in Türkiye as a case study. Relevant literature and statistical data were analyzed to provide a detailed examination of the specific impacts in Türkiye. In addition, the study aims to support national goals and increase resilience to future crises by providing recommendations for the stakeholders and national policy makers. These recommendations include the adoption of digitalization and blockchain technologies, regional expansion of maritime trade, considering storage strategies and developing logistics infrastructure.

ECONOMIC BENEFITS OF DEPLOYING FOLDABLE CONTAINERS: REDUCING BUNKER AND CONTAINER MANAGEMENT COSTS IN A MULTI-PORT SHIPPING NETWORK

This study seeks to explore the effectiveness of employing foldable containers (FLDs) in liner shipping to reduce relocation and the empty containers and bunker costs (BCs) associated with ship operations. This resolves a minimum-cost multi-commodity network flow problem by optimizing container fleet size and empty container relocation in a multi-port shipping service network. Port handling time and sailing speed provided by obtained optimal solutions enable the determination of ship BCs as a secondary step. The numerical experiments demonstrate the comparative effect of FLDs against standard ones on the reduction of the costs of empty containers and containership bunker oil.